In chiplet-based design we continue the march of Moore’s Law by scaling what we can put in a semiconductor package beyond the boundaries of what we can build on a single die. This style is already gaining traction in AI applications, high performance computing, and automotive, each of which aims to scale out to highly integrated … Read More
Keeping up with the furious pace of AI innovation probably doesn’t allow a lot of time for deep analysis across many use cases. However I can’t help feeling we’re sacrificing quality and ultimately end user acceptance of AI by prioritizing new capabilities over rigorous productization. I am certain that product companies do rigorous… Read More
In engineering development, automated code generation as a pair programming assistant is high on the list of targets for GenAI applications. For hardware design obvious targets would be to autogenerate custom RTL functions or variants on standard functions, or to complete RTL snippets as an aid to human-driven code generation.… Read More
This time let’s see if we can stir up some lively debate. Cocotb isn’t new but it is an interesting alternative to mainstream testing methodologies. Paul Cunningham (GM, Verification at Cadence), Raúl Camposano (Silicon Catalyst, entrepreneur, former Synopsys CTO and lecturer at Stanford, EE292A) and I continue our series … Read More
By now everyone knows that AI has become the all-consuming driver in tech and that NVIDIA GPU-based platforms are the dominant enabler of this revolution. Datacenters worldwide are stuffed with such GPUs, serving AI workloads from automatically drafting emails and summarizing meetings to auto-creating software and controlling… Read More
I have seen a flood of verification announcements around directly reading product specs through LLM methods, and from there directly generating test plans and test suite content to drive verification. Conceptually automating this step makes a lot of sense. Carefully interpreting such specs even today is a largely manual task,… Read More
The immediate appeal of large language models (LLMs) is that you can ask any question using natural language in the same way you would ask an expert, and it will provide an answer. Unfortunately, that answer may be useful only in simple cases. When posing a question we often implicitly assume significant context and skate over ambiguities.… Read More
This isn’t a deep article. I only want to help head off possible confusion over this term. I have recently seen “vibe coding” pop up in discussions around AI for code generation. The name is media-friendly giving it some stickiness in the larger non-technical world, always a concern when it comes to anything AI. The original intent… Read More
We have a shortage of reference designs to test detection of security vulnerabilities. An LLM-based method demonstrates how to fix that problem with structured prompt engineering. Paul Cunningham (GM, Verification at Cadence), Raúl Camposano (Silicon Catalyst, entrepreneur, former Synopsys CTO and lecturer at Stanford,… Read More
Given my SpyGlass background I always keep an eye out for new ideas that might be emerging in static and formal verification. Whatever can be covered through stimulus-free analysis reduces time that needn’t be wasted in dynamic analysis, also adding certainty to coverage across that range. Still, advances don’t come easily. … Read More
